Autonomous system (mathematics)

About: Autonomous system (mathematics) is a research topic. Over the lifetime, 1648 publications have been published within this topic receiving 38373 citations.

Intelligent behavior generator for autonomous mobile robots using planning-based AI decision making and supervisory control logic

Abstract: In earlier research the Center for Self-Organizing and Intelligent Systems (CSOIS) at Utah State University (USU) have been funded by the US Army Tank-Automotive and Armaments Command's (TACOM) Intelligent Mobility Program to develop and demonstrate enhanced mobility concepts for unmanned ground vehicles (UGVs). One among the several out growths of this work has been the development of a grammar-based approach to intelligent behavior generation for commanding autonomous robotic vehicles. In this paper we describe the use of this grammar for enabling autonomous behaviors. A supervisory task controller (STC) sequences high-level action commands (taken from the grammar) to be executed by the robot. It takes as input a set of goals and a partial (static) map of the environment and produces, from the grammar, a flexible script (or sequence) of the high-level commands that are to be executed by the robot. The sequence is derived by a planning function that uses a graph-based heuristic search (A* -algorithm). Each action command has specific exit conditions that are evaluated by the STC following each task completion or interruption (in the case of disturbances or new operator requests). Depending on the system's state at task completion or interruption (including updated environmental and robot sensor information), the STC invokes a reactive response. This can include sequencing the pending tasks or initiating a re-planning event, if necessary. Though applicable to a wide variety of autonomous robots, an application of this approach is demonstrated via simulations of ODIS, an omni-directional inspection system developed for security applications.

A framework for the natural-language-perception-based creative control of unmanned ground vehicles

Abstract: Mobile robots must often operate in an unstructured environment cluttered with obstacles and with many possible action paths. That is why mobile robotics problems are complex with many unanswered questions. To reach a high degree of autonomous operation, a new level of learning is required. On the one hand, promising learning theories such as the adaptive critic and creative control have been proposed, while on other hand the human brain’s processing ability has amazed and inspired researchers in the area of Unmanned Ground Vehicles but has been difficult to emulate in practice. A new direction in the fuzzy theory tries to develop a theory to deal with the perceptions conveyed by the natural language. This paper tries to combine these two fields and present a framework for autonomous robot navigation. The proposed creative controller like the adaptive critic controller has information stored in a dynamic database (DB), plus a dynamic task control center (TCC) that functions as a command center to decompose tasks into sub-tasks with different dynamic models and multi-criteria functions. The TCC module utilizes computational theory of perceptions to deal with the high levels of task planning. The authors are currently trying to implement the model on a real mobile robot and the preliminary results have been described in this paper.

An autonomous system used in the DARPA Grand Challenge

Abstract: An autonomous system was designed and constructed to traverse the hostile desert terrain in the DARPA Grand Challenge robotics competition. Our autonomous system used GPS measurements in conjunction with a laser range finder to determine the passable terrain and to negotiate around natural and man-made obstacles on the route. The primary navigation portion of this system uses a scalable, heuristic array of data, and determines the preferred heading of the vehicle, factoring desired heading from a GPS sensor and obstacle data from a laser range finder. When implemented, this system demonstrated the capability to pass stationary vehicles and navigate sloping terrain while negotiating around fences and towers between sequential GPS waypoints.

Autonomous Bus Operation Alternatives in Urban Areas Using Fuzzy Dombi-Bonferroni Operator Based Decision Making Model

Abstract: Advances in V2V, V2I, and autonomous vehicle technologies have made autonomous buses possible in cities. Soon, autonomous bus operations will be common in urban areas, which will improve sustainability, safety, and the city’s technology. These buses have different operation types. Each operation has advantages and disadvantages. Therefore, the goal of this study is to serve as a guide for decision-makers during the transition to autonomous buses. Four alternatives are evaluated based on eleven criteria organized under four main aspects, namely autonomous buses for special uses, autonomous buses for last-mile uses, autonomous cars in mixed traffic, and autonomous buses in closed systems. We propose an Ordinal Priority Approach (OPA) method for determining the criteria weights and application of fuzzy Dombi Bonferroni (DOBI) methodology for the evaluation of alternatives. When compared to the other three alternatives in this study, the results show that deploying autonomous buses in mixed traffic is the most advantageous option.